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Read the following description of a data set.\newlineA food scientist wants to understand how much sugar children like in their desserts. He sets up a study where children taste cups of vanilla pudding with varying sweetness and rate each cup on a scale of 11 to 1010, where 1010 is excellent.For each tasting, the food scientist writes down the grams of sugar in the pudding cup, xx, as well as its rating, yy.The least squares regression line of this data set is:y=0.216x4.085y = 0.216x - 4.085\newlineComplete the following sentence:\newlineFor each additional gram of sugar in a pudding cup, the least squares regression line predicts its rating will increase by ___.

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Q. Read the following description of a data set.\newlineA food scientist wants to understand how much sugar children like in their desserts. He sets up a study where children taste cups of vanilla pudding with varying sweetness and rate each cup on a scale of 11 to 1010, where 1010 is excellent.For each tasting, the food scientist writes down the grams of sugar in the pudding cup, xx, as well as its rating, yy.The least squares regression line of this data set is:y=0.216x4.085y = 0.216x - 4.085\newlineComplete the following sentence:\newlineFor each additional gram of sugar in a pudding cup, the least squares regression line predicts its rating will increase by ___.
  1. Identify slope: Identify the slope of the least squares regression line from the given equation y=0.216x4.085y = 0.216x - 4.085. The slope is the coefficient of xx, which in this case is 0.2160.216.
  2. Interpret slope: Interpret the slope of the regression line. The slope value of 0.2160.216 means that for each additional gram of sugar in the pudding cup, the predicted rating increases by 0.2160.216 points on the scale of 11 to 1010.

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